### Abstract

Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of spatially shifted linear filters. These filters cannot be estimated using spike-triggered averaging (STA). Subspace methods such as spike-triggered covariance (STC) can recover multiple filters, but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first linear stage is a set of shifted ('convolutional') copies of a common filter, and the first nonlinear stage consists of rectifying scalar nonlinearities that are identical for all filter outputs. We refer to these initial LN elements as the 'subunits' of the receptive field. The second linear stage then computes a weighted sum of the responses of the rectified subunits. We present a method for directly fitting this model to spike data, and apply it to both simulated and real neuronal data from primate V1. The subunit model significantly outperforms STA and STC in terms of cross-validated accuracy and efficiency.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |

Pages | 3104-3112 |

Number of pages | 9 |

Volume | 4 |

State | Published - 2012 |

Event | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: Dec 3 2012 → Dec 6 2012 |

### Other

Other | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
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Country | United States |

City | Lake Tahoe, NV |

Period | 12/3/12 → 12/6/12 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems
- Signal Processing

### Cite this

*Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012*(Vol. 4, pp. 3104-3112)

**Efficient and direct estimation of a neural subunit model for sensory coding.** / Vintch, Brett; Zaharia, Andrew D.; Movshon, J. Anthony; Simoncelli, Eero.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012.*vol. 4, pp. 3104-3112, 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012, Lake Tahoe, NV, United States, 12/3/12.

}

TY - GEN

T1 - Efficient and direct estimation of a neural subunit model for sensory coding

AU - Vintch, Brett

AU - Zaharia, Andrew D.

AU - Movshon, J. Anthony

AU - Simoncelli, Eero

PY - 2012

Y1 - 2012

N2 - Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of spatially shifted linear filters. These filters cannot be estimated using spike-triggered averaging (STA). Subspace methods such as spike-triggered covariance (STC) can recover multiple filters, but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first linear stage is a set of shifted ('convolutional') copies of a common filter, and the first nonlinear stage consists of rectifying scalar nonlinearities that are identical for all filter outputs. We refer to these initial LN elements as the 'subunits' of the receptive field. The second linear stage then computes a weighted sum of the responses of the rectified subunits. We present a method for directly fitting this model to spike data, and apply it to both simulated and real neuronal data from primate V1. The subunit model significantly outperforms STA and STC in terms of cross-validated accuracy and efficiency.

AB - Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of spatially shifted linear filters. These filters cannot be estimated using spike-triggered averaging (STA). Subspace methods such as spike-triggered covariance (STC) can recover multiple filters, but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first linear stage is a set of shifted ('convolutional') copies of a common filter, and the first nonlinear stage consists of rectifying scalar nonlinearities that are identical for all filter outputs. We refer to these initial LN elements as the 'subunits' of the receptive field. The second linear stage then computes a weighted sum of the responses of the rectified subunits. We present a method for directly fitting this model to spike data, and apply it to both simulated and real neuronal data from primate V1. The subunit model significantly outperforms STA and STC in terms of cross-validated accuracy and efficiency.

UR - http://www.scopus.com/inward/record.url?scp=84877726462&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84877726462&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84877726462

SN - 9781627480031

VL - 4

SP - 3104

EP - 3112

BT - Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

ER -